Cardiac size is useful diagnostic information obtained from chest radiographs. Abnormal enlargement of the heart is often detected initially in reviews of these images. A conventional method of assessing cardiac enlargement is by measurement of the cardiothoracic ratio (CTR) (see to Sutton, “A Textbook of Radiology and Imaging,” 4th Edition, Vol. 1, pp. 554-556 Churchill Livington, 1987; and Burgener et al., “Differential Diagnosis in Conventional Radiology,” pp. 259-292 (George Thieme Verlag, Thieme-Stratton, 1985) which is a ratio of the transverse diameter of the cardiac shadow to the transverse diameter of the thorax at the highest level of the diaphragm (refer to Danzer, “The Cardiothoracic Ratio An Index of Cardiac Enlargement,” Am. J. Med. Sci. 157:513-524, 1919).
The concept of automated computer analysis of radiographic images dates back to the 1960's. An early attempt at automated determination of the CTR was that of Meyers et al. (Radiology 83:1029-1033 1964) wherein the spatial signature from digitized chest images was used and the edges of the heart and lung was determined from the first derivative of the signature. (See also Becker et al., IEEE Trans. Biomed. Eng. BME-11:67-72, 1964.) Hall et al. (Radiology 101:497-509, 1971) and Kruger et al. (IEEE Trans. Biomed. Eng. BME-19:174-186, 1972) developed an algorithm for automated diagnosis of rheumatic heart disease, wherein the CTR and other cardiac parameters were computed. The approach included determining a cardiac rectangle from analysis of the signatures and their derivatives, and then estimating the cardiac shadow by thresholding the image on the basis of analysis of the histogram.
Sezaki et al. (IEEE Trans. Biomed. Eng. BME-20:248-253, 1973) developed an algorithm with which the CTR was computed for about 1 sec to provide radiologists with a practical instrument with which patients with abnormal hearts could be detected automatically by analysis of mass-screening chest radiographs.
Paul et al. (IEEE Trans. Biomed. Eng. BME-21:441-451, 1974) computed the total lung volume by analyzing AP and lateral chest images, in which they determined the cardiac boundary by using the Gaussian-weighted derivative edge detection technique.
US Patent Application No. 2004/0153128 (Suresh el al) is directed to method and system for image processing and contour assessment. One embodiment relates to a computerized method of facilitating cardiac intervention.
U.S. Pat. No. 5,072,384 (Doi et al.) relates to a method and system for automated computerized analysis of sizes of hearts and lungs in digital chest radiographs, comprising: (1) detecting plural right and left cardiac boundary points in the cardiac contour; (2) fitting a predetermined model function to the detected cardiac boundary points to derive a completed cardiac contour based on the fitted model function; (3) using a shift-variant sinusoidal function as said predetermined model function; and (4) producing a representation of the completed cardiac contour. Specifically, after extracting edge points based on edge gradients, Doi performs the following operations: (1) selecting plural of those possible cardiac boundary points, which are adjacent said diaphragm edge points as cardiac boundary points; (2) fitting said selected cardiac boundary points to a predetermined model function in the form of a circle using a least squares method to derive a first circle fitted to said selected boundary points; (3) selecting second and third circles concentric with said first circle and respectively having diameters larger and smaller by a predetermined amount than the diameter of said first circle; (4) detecting which of the possible cardiac boundary points are located in a region between said second and third circles; and (4) selecting those possible cardiac boundary points detected as being located in the region between said second and third circles as cardiac boundary points. Doi measures the size of the cardiac contour fitted to the cardiac boundary points. DOI also describes the use of a shift-variant sinusoidal function as the model function fitted to the right and left cardiac boundary points determined from the digital chest radiograph.
However, while the human heart has a somewhat constant shape, Doi's parametric models (e.g., circle, or cosine functions) are over-simplified and over-constrained so that they are frequently inadequate to handle the amount of shape variations between different individuals, between different periods of the heartbeat cycle, and between different amounts of occlusion.
Recently, a statistical model referred to as an active shape model (ASM) has been applied to segmentation of lung fields in digital radiographs. ASM is described in Ginneken et al. (B. V. Ginneken, A. F. Frangi, J. J. Staal, B. M. H. Romeny, and M. A. Viergever, “Active shape model segmentation with optimal features,” IEEE Trans. on Medical Imaging, vol. 21, no. 8, August 2002.).
A difference between lung fields and the heart is that the lung fields are completely un-occluded in a chest radiograph, while the heart is severely occluded. It is estimated that, on average, 20-40% of the heart boundary is visible because of the mediastinum, which is in front of the heart in PA exams (posterior-anterior) or behind the heart in AP (anterior-posterior) exams. Because x-ray imaging is a projection-based imaging modality (compared to cross section-based imaging modalities such as CT and MRI), the heart appears as an occluded object in a radiograph regardless of whether it is a PA or AP exam. Consequently, ASM is not directly applicable for a severely occluded object such as the heart.
Accordingly, there exists a need for a method for segmenting occluded anatomic structures in a medical image produced by a projection-based medical imaging modality.
Further, there exists a need for a statistical model for such structures in order to handle large statistical variations in the shape of the target structure due to various factors.